Gene expression during development is often exclusively studied at the level of transcription. However, an additional and essential level of control occurs via post-transcriptional mRNA decay. Regulation of mRNA decay is particularly important during nervous system development, where the structure of neurons requires selective stabilization of mRNAs far from their site of synthesis and the generation of cellular diversity requires the programmed decay of mRNAs that regulate proliferation and differentiation. Defective mRNA decay has been implicated in many neurological birth defects, including fragile X-syndrome (the most common form of hereditary mental impairment) and spinal muscular atrophy (a leading genetic cause of infant mortality). The study of mRNA decay during embryonic development has previously been hindered by the lack of methods allowing in vivo, cell type-specific measurements of transcript stability. We have developed a technique (based on TU-tagging methods) that overcomes this technical challenge and allows neural-specific, genome-wide measurements of mRNA decay in intact Drosophila embryos. This technique provides the foundation for a systems-level approach that will allow us to construct a Neural Development mRNA Decay Network. This network will contain the following information: the decay rates of all mRNAs expressed in the embryonic nervous system, the cis-elements that target these mRNAs for decay, the trans-acting RNA-binding proteins and miRNAs that bind these cis-elements, and the spatial dynamics of mRNA - RNA-binding protein interactions. This information will be obtained using a systems-level approach that combines genome-wide analysis of mRNA decay rates in wildtype and RBP mutant embryos, computational identification of candidate linear and structural cis-elements, molecular genetic definition of cis element effects on transcript stability in vivo, biochemical analysis of RBP-mRNA interactions in vitro and in vivo, and imaging of fluorescent mRNA - RBP interactions within neurons. This work will generate a comprehensive and predictive network map of neural mRNA decay dynamics, thus filling a significant gap in current models of gene expression during neural development.